Bibliographic record
Abstract
Contemporary decisions about the management of populations, public services, security, and the environment are increasingly made through knowledge gleaned from "big data" and its attendant infrastructures and algorithms. Though often described as "raw," this data is produced by techniques of measurement that are imbued with judgments and values that dictate what is counted and what is not, what is considered the best unit of measurement, and how different things are grouped together and "made" into a measureable entity. In this paper, we analyze these politics of measurement and how they relate to action through two case studies involving high stake public health measurements where experts intentionally leverage measurement to change definitions of harm and health. That is, they use measurement for activism. The case studies offer a framework for thinking about of how the politics of measurement are present in user interfaces. It is usually assumed that the human element has been scrubbed from the database and that significant political and subjective interventions come from the analysis or use of data after the fact. Instead, we argue that human-computer interactions start before the data reaches the computer because various measurement interfaces are the invisible premise of data and databases, and these measurements are political.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.147 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.142 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".